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Recommender system construction using latent semantic analysis and data mining methods one-commerce data

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2019
Özer, Arif Görkem
Recommender systems are developed to provide better recommendations to users of e-commerce applications. In addition to this goal, e-commerce applications benefit from their recommender systems to show advertisements to users, apply discounts on specific items. The task of recommending an item to a user is always a challenge; luckily, there are many methods developed to complete this task such as collaborative filtering, association rule mining etc. These methods mainly look at the co-occurrence of items; however, we think that user behavior on different items should be extracted by doing latent semantic analysis on the data. Latent semantic analysis is used for understanding the context of a text, we think that it can be used for providing recommendations by processing transactional data. The data used throughout this thesis work consists of transactions made in various e-commerce companies. In this thesis work, existing methods and proposed recommendation methods are examined and recommendation results on this data are shown.